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Results for “biological big data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient’s phenotype. Here, the development of two new ontologies — the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation — will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗
DOE OSTI2022

Small partners, big impacts

Bioactive metabolites mediate interactions between hosts and their microbiomes, often to improve the fitness of the holobionts. However, the mechanistic basis of these interactions and their contributions to fitness are poorly understood owing to their multifaceted nature. Finally, recent studies integrating metabolomics with genomic and functional data are beginning to fill this gap.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Artificial intelligence in cancer research, diagnosis and therapy

Artificial intelligence and machine learning techniques are breaking into biomedical research and health care, which importantly includes cancer research and oncology, where the potential applications are vast. These include detection and diagnosis of cancer, subtype classification, optimization of cancer treatment and identification of new therapeutic targets in drug discovery. While big data used to train machine learning models may already exist, leveraging this opportunity to realize the full promise of artificial intelligence in both the cancer research space and the clinical space will first require significant obstacles to be surmounted. In this Viewpoint article, we asked four experts for their opinions on how we can begin to implement artificial intelligence while ensuring standards are maintained so as transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

'Omics and Big Data in Harmful Algal Bloom Research

Phytoplankton, a group including eukaryotic microalgae and cyanobacteria, play a crucial climate role converting CO 2 into organic carbon through global primary production. They support a wide range of life, both freshwater and marine, from zooplankton to fish and mammals. While they are essential in nutrient cycles, certain phytoplankton species can proliferate excessively under favorable conditions, leading to harmful algal blooms (HABs) that pose significant threats to human and ecosystem health through the toxins they produce.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Through the lens of bioenergy crops: advances, bottlenecks, and promises of plant engineering

Advances in engineering of bioenergy crops were driven over the past years by adapting technological breakthroughs and accelerating conventional applications but also exposed intriguing challenges. New tools revealed rich interconnectivity in the exponentially growing and dynamic 'big' omics data' of metabolomes, transcriptomes, and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single cell). Insights enabled fresh hypotheses and stimulated disciplines such as functional genomics with discovery of broad regulatory networks and their determinants, that is, DNA parts, including promoters, regulatory elements, and transcription factors. Their rational design, assembly into increasingly complex blueprints, and installation into diverse chassis is an existing frontier that may benefit from emerging technologies to address bottlenecks. Interweaving nature-inspired to fully synthetic parts has already allowed building of fine-tuned regulatory circuits, or new-to-nature metabolic routes insulated from the biological context of the chassis species. Similarly, developments and the evolving need for unifying principles in plant transformation and species-agnostic technologies highlight future opportunities for engineering the next generation of bioenergy plants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO 2 , and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO 2 , and soil moisture) on canopy processes (i.e. net CO 2 assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO 2 assimilation and increased air temperature. Net CO 2 assimilation increased at higher atmospheric CO 2 , while decreasing soil moisture resulted in lower leaf water potential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cross‐Cultural Validation of the Binge Eating Disorder Screener‐7 ( BEDS ‐7) Across 42 Countries

ABSTRACT Objective This study aimed to evaluate the reliability and validity of the Binge Eating Disorder Screener‐7 (BEDS‐7) across 42 countries and 26 languages, assessing its reliability and validity as a screening tool for binge‐eating disorder (BED) in diverse cultural contexts. Specifically, it sought to enhance early recognition of BED symptoms in primary care settings globally, contributing to a standardized framework for assessing BED. Method The International Sex Survey, a cross‐sectional online study, was conducted in 42 countries and 26 languages. A diverse community sample of 82,243 participants, aged 18 years or older, completed the BEDS‐7 and measures of sexuality, mental health, substance use, and sociodemographic characteristics. Confirmatory factor analyses and tests of measurement invariance were employed to evaluate the reliability and validity of the BEDS‐7 across languages, countries, genders, and sexual orientations. Results The BEDS‐7 demonstrated scalar factorial invariance across languages and countries, indicating consistent factor loadings and item intercepts. In contrast, the screener showed residual invariance across gender and sexual orientation groups, supporting its robustness across these demographics. Kruskal–Wallis tests revealed significant differences in BED symptoms across languages, countries, genders, and sexual orientations, with the highest BED scores observed among queer, pansexual, and gender‐diverse individuals. The BEDS‐7 also demonstrated adequate reliability (Cronbach's alpha > 0.80) and moderate criterion validity. Discussion The findings provide further evidence of the reliability and validity of the BEDS‐7 as a potential screening tool for identifying probable cases of BED globally, facilitating early intervention in primary care settings.

Gewirtz‐Meydan, Ateret [School of Social Work, Fac↗
DOE OSTI2024

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants

Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses. In this review, advanced applications of ML and DL in plant biotic and abiotic stress management have been summarized.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

We report that computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Major Revisions in Arthropod Phylogeny Through Improved Supermatrix, With Support for Two Possible Waves of Land Invasion by Chelicerates

Deep phylogeny involving arthropod lineages is difficult to recover because the erosion of phylogenetic signals over time leads to unreliable multiple sequence alignment (MSA) and subsequent phylogenetic reconstruction. One way to alleviate the problem is to assemble a large number of gene sequences to compensate for the weakness in each individual gene. Such an approach has led to many robustly supported but contradictory phylogenies. A close examination shows that the supermatrix approach often suffers from two shortcomings. The first is that MSA is rarely checked for reliability and, as will be illustrated, can be poor. The second is that, to alleviate the problem of homoplasy at the third codon position of protein-coding genes due to convergent evolution of nucleotide frequencies, phylogeneticists may remove or degenerate the third codon position but may do it improperly and introduce new biases. We performed extensive reanalysis of one of such “big data” sets to highlight these two problems, and demonstrated the power and benefits of correcting or alleviating these problems. Our results support a new group with Xiphosura and Arachnopulmonata (Tetrapulmonata + Scorpiones) as sister taxa. This favors a new hypothesis in which the ancestor of Xiphosura and the extinct Eurypterida (sea scorpions, of which many later forms lived in brackish or freshwater) returned to the sea after the initial chelicerate invasion of land. Our phylogeny is supported even with the original data but processed with a new “principled” codon degeneration. We also show that removing the 1673 codon sites with both AGN and UCN codons (encoding serine) in our alignment can partially reconcile discrepancies between nucleotide-based and AA-based tree, partly because two sequences, one with AGN and the other with UCN, would be identical at the amino acid level but quite different at the nucleotide level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Embracing fine-root system complexity in terrestrial ecosystem modeling

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine-root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data-model gap in still extremely uncertain models. In this work, we propose a three-pool structure comprising transport and absorptive fine roots with mycorrhizal fungi (TAM) to model vertically resolved fine-root systems across organizational and spatial–temporal scales. Emerging from a conceptual shift away from arbitrary homogenization, TAM builds upon theoretical and empirical foundations as an effective and efficient approximation that balances realism and simplicity. A proof-of-concept demonstration of TAM in a big-leaf model both conservatively and radically shows robust impacts of differentiation within fine-root systems on simulating carbon cycling in temperate forests. Theoretical and quantitative support warrants exploiting its rich potentials across ecosystems and models to confront uncertainties and challenges for a predictive understanding of the biosphere. Echoing a broad trend of embracing ecological complexity in integrative ecosystem modeling, TAM may offer a consistent framework where modelers and empiricists can work together toward this grand goal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Big-Data-Driven Geo-Spatiotemporal Correlation Analysis between Precursor Pollen and Influenza and its Implication to Novel Coronavirus Outbreak

Although studies of many respiratory viruses and pollens are often framed by both seasonal and health related perspectives, pollen has yet to be extensively examined as an important covariate to seasonal respiratory viruses (SRVs) in any context, including a causal one. This study contributes to those goals through an investigation of SRVs and pollen counts at selected regions across the Western Hemisphere. Two complementary decadal-scaled geospatial profiles were developed. One laterally spanned the US and was anchored by detailed pollen information for Albuquerque, New Mexico. The other straddled the equator to include Fortaleza, Brazil. We found that the geospatial and climatological patterns of pollen advancement and decline across the US every year presented a statistically significant correlation to the subsequent emergence and decline of SRVs. Other significant covariates included winds, temperatures, and atmospheric moisture. Our study indicates that areas of the US with lower geostrophic wind baselines are typically areas of persistently higher and earlier influenza like illness (ILI) cases. In addition to that continental- scaled contrast, many sites indicated seasonal highs of geostrophic winds and ILI which were closely aligned. These observations suggest extensive scale-dependent connectivity of viruses to geostrophic circulation. Pollen emergence and its own scale-dependent circulation may contribute to the geospatial and seasonal patterns of ILI. We explore some uncertainties associated with this investigation, and consider the possibility that in a temperate climate, following a Spring pollen emergence, a resulting increase in pollen triggered human Immunoglobulin E (IgE) antibodies may suppress ILIs for several months.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 °C (V c,max25 ) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Floating Island International PHASE I FINAL TECHNICAL REPORT (Advancing the Development of Floating Solar-Powered Nanobubble Aeration Systems for Use with Floating Treatment Wetlands in Natural and Man-Made Waterbodies)

More and more freshwater lakes are suffering from algae blooms, which deprive water of oxygen and lead to widespread loss of aquatic life and production of dangerous toxins. When oxygen is lacking, methane is generated in the sediments as algae is decomposed; it has been calculated that more than half of global methane emissions come from nutrient-impaired freshwater systems (Beaulieu, 2021). Nutrients, mainly from agricultural run-off, feed algae blooms and are exacerbated by warming related to climate change. Artificial aeration is frequently used to restore oxygen to a waterbody. But diffuser aeration is inefficient and expensive, needs on-site grid power, and is failing to keep pace with the demands of water as it gets warmer with climate change. In the last five years, nanobubble aeration has been introduced into freshwater applications and shows promise for rapidly increasing dissolved oxygen effectively throughout the water column. Nanobubbles deliver oxygen in bubbles that have no buoyancy, so they stay in water longer and release their oxygen more fully, compared to large bubbles that rise and burst at the surface. The prospect of this new technology answering the deficiencies of diffuser aeration drove us to initiate the current project. During our Phase I period, we have successfully tested a nanobubble aeration system that can super-saturate oxygen levels. It is operated on solar power, and both the nanobubbler and solar array are mounted on a proprietary floating island platform, that also performs biological nutrient recycling. A rudimentary system was assembled and tested on a 6.5-acre research lake at our headquarters in Montana, where it was subjected to a summer drought that reduced water levels significantly, periods of severe cold in winter, and spring conditions that included heavy rainfall and violent thunderstorms. Our twice-weekly sampling throughout the project showed that dissolved oxygen levels rose rapidly and were maintained throughout winter and spring, well into June. The nanobubbler was able to run on solar power for long periods. Methane levels were tested periodically and found to decrease as oxygen increased. The nanobubbles appeared to have no adverse impact on fish or other aquatic life exposed to them. The stability of the installation survived the weather conditions. The many problems we encountered taught us what we need to improve in the next iteration. Towards the end of our Phase I, we were awarded a supplementary state grant that enabled us to acquire a new nanobubble system for testing that runs on DC, is extremely efficient and has few moving parts. We plan to team this with low-profile solar panels, lithium batteries and a controller that provides real-time data. The ultimate goal of this project is to commercialize an affordable and scalable lake management system that will oxygenate water from the surface down to the sludge. It will enliven fisheries, prevent toxic algae blooms and – most importantly for the fate of our planet - inhibit the production and release of methane from the sediments. We anticipate that as carbon credits expand to include methane, the cost of remediating waterbodies that produce methane will be offset. We firmly believe that this is a practical technology that works and will make a big difference when fully implemented.

14 SOLAR ENERGY↗
DOE OSTI2025

Enhancing DESI DR1 full-shape analyses using HOD-informed priors

We present an analysis of DESI Data Release 1 (DR1) that incorporates Halo Occupation Distribution (HOD)-informed priors into Full-Shape (FS) modeling of the power spectrum based on cosmological perturbation theory (PT). By leveraging physical insights from the galaxy-halo connection, these HOD-informed priors on nuisance parameters substantially mitigate projection effects in extended cosmological models that allow for dynamical dark energy. The resulting credible intervals now encompass the posterior maximum from the baseline analysis using gaussian priors, eliminating a significant posterior shift observed in baseline studies. In the ΛCDM framework, a combined DESI DR1 FS information and constraints from the DESI DR1 baryon acoustic oscillations (BAO) — including Big Bang Nucleosynthesis (BBN) constraints and a weak prior on the scalar spectral index — yields Ω m = 0.2994 ± 0.0090 and σ 8 = 0.836$^{+0.024}_{-0.027}$, representing improvements of approximately 4% and 23% over the baseline analysis, respectively. For the w 0 w a CDM model, our results from various data combinations are highly consistent, with all configurations converging to a region with w 0 > -1 and w a < 0. This convergence not only suggests intriguing hints of dynamical dark energy but also underscores the robustness of our HOD-informed prior approach in delivering reliable cosmological constraints.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Assessing the cumulative effects of nearshore habitat restoration actions for multiple populations of juvenile salmon in Whidbey Basin, Washington: foundation and approach for synthesis and evaluation

Ecosystem restoration is a common tool for re-establishing ecosystem processes, structures, and functions to improve biodiversity and services in coastal and estuarine ecosystems. In the Salish Sea, salmon habitats have been fragmented, reduced in size, and diminished in quality, and the ecosystem processes that form and sustain these habitats have been degraded and disrupted as well. This loss is especially prevalent in estuaries, where up to 90% of former salmon habitat has been lost or compromised. Salmon species are integral to the identities and cultures of people in the Pacific Northwest, yet salmon abundances remain at historic lows, especially in urbanized areas. Recent investments in restoration are creating rearing habitat and repairing lost ecosystem function. However, restoration efforts in this region have largely proceeded at the site scale, with less attention to big-picture thinking regarding how restoration will effectively recover degraded or lost habitats for target species. As a result, no landscape-scale evaluation program exists, and the cumulative benefits of multiple interventions are unknown. We describe innovative methods for science synthesis related to the evaluation of cumulative effects of ecosystem restoration for Pacific salmon, using years of existing, but disparate data. Building from previous work on cumulative effects evaluation and incorporating a hierarchy of hypotheses approach, we propose using causal inference across numerous hypotheses in a framework to assess the cumulative benefits to Pacific salmon from multiple estuarine restoration projects. We present the framework as a method that can be used to address many complex questions and provide examples from the Salish Sea where the approach is being implemented. The framework draws on science synthesis from numerous fields and uses a hierarchy of hypotheses, causal analysis at multiple scales, and a new hierarchy of synthesis for assessing multiple lines of evidence documenting restoration effects on Pacific salmon. We propose causal inference to synthesize dissimilar data streams, in our case, to identify various manifestations of cumulative effects of restoration and benefits to salmon, and to further inform restoration and recovery planning. A unifying framework would allow for the detection of thresholds at which restoration provides measurable improvement and would greatly advance understanding of the effects of restoration on ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗